{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import *\n",
    "\n",
    "def loadDataSet(fileName):      #general function to parse tab -delimited floats\n",
    "    dataMat = []                #assume last column is target value\n",
    "    fr = open(fileName)\n",
    "    for line in fr.readlines():\n",
    "        curLine = line.strip().split('\\t')\n",
    "        fltLine = list(map(float,curLine)) #map all elements to float()\n",
    "        dataMat.append(fltLine)\n",
    "    return dataMat\n",
    "\n",
    "def distEclud(vecA, vecB):\n",
    "    return sqrt(sum(power(vecA - vecB, 2))) #la.norm(vecA-vecB)\n",
    "\n",
    "def randCent(dataSet, k):\n",
    "    n = shape(dataSet)[1]\n",
    "    centroids = mat(zeros((k,n)))#create centroid mat\n",
    "    for j in range(n):#create random cluster centers, within bounds of each dimension\n",
    "        minJ = min(dataSet[:,j]) \n",
    "        rangeJ = float(max(dataSet[:,j]) - minJ)\n",
    "        centroids[:,j] = mat(minJ + rangeJ * random.rand(k,1))\n",
    "    return centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 1.658985,  4.285136],\n",
       "        [-3.453687,  3.424321],\n",
       "        [ 4.838138, -1.151539],\n",
       "        [-5.379713, -3.362104],\n",
       "        [ 0.972564,  2.924086],\n",
       "        [-3.567919,  1.531611],\n",
       "        [ 0.450614, -3.302219],\n",
       "        [-3.487105, -1.724432],\n",
       "        [ 2.668759,  1.594842],\n",
       "        [-3.156485,  3.191137],\n",
       "        [ 3.165506, -3.999838],\n",
       "        [-2.786837, -3.099354],\n",
       "        [ 4.208187,  2.984927],\n",
       "        [-2.123337,  2.943366],\n",
       "        [ 0.704199, -0.479481],\n",
       "        [-0.39237 , -3.963704],\n",
       "        [ 2.831667,  1.574018],\n",
       "        [-0.790153,  3.343144],\n",
       "        [ 2.943496, -3.357075],\n",
       "        [-3.195883, -2.283926]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datMat=mat(loadDataSet('testSet.txt'))\n",
    "datMat[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[-5.379713]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(datMat[:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[-4.232586]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(datMat[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 5.1904]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(datMat[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 4.838138]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(datMat[:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 2.58914712,  1.61518293],\n",
       "        [ 4.73583437,  2.78832966]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randCent(datMat, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.184632816681332"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distEclud(datMat[0], datMat[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kMeans(dataSet, k, distMeas=distEclud, createCent=randCent):\n",
    "    m = shape(dataSet)[0]\n",
    "    clusterAssment = mat(zeros((m,2)))#create mat to assign data points \n",
    "                                      #to a centroid, also holds SE of each point\n",
    "    centroids = createCent(dataSet, k)\n",
    "    clusterChanged = True\n",
    "    while clusterChanged:\n",
    "        clusterChanged = False\n",
    "        for i in range(m):#for each data point assign it to the closest centroid\n",
    "            minDist = inf; minIndex = -1\n",
    "            for j in range(k):\n",
    "                distJI = distMeas(centroids[j,:],dataSet[i,:])\n",
    "                if distJI < minDist:\n",
    "                    minDist = distJI; minIndex = j\n",
    "            if clusterAssment[i,0] != minIndex: clusterChanged = True\n",
    "            clusterAssment[i,:] = minIndex,minDist**2\n",
    "        print(centroids)\n",
    "        for cent in range(k):#recalculate centroids\n",
    "            ptsInClust = dataSet[nonzero(clusterAssment[:,0].A==cent)[0]]#get all the point in this cluster\n",
    "            centroids[cent,:] = mean(ptsInClust, axis=0) #assign centroid to mean \n",
    "    return centroids, clusterAssment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.95403332 -3.43319756]\n",
      " [-1.52306118 -4.01661356]\n",
      " [-0.01385685  1.55550449]\n",
      " [ 4.39912789  1.85559496]]\n",
      "[[ 1.73578167 -3.10972617]\n",
      " [-3.64094524 -2.98948247]\n",
      " [-1.54544992  2.85706781]\n",
      " [ 3.29182316  1.93982137]]\n",
      "[[ 2.44502437 -2.980011  ]\n",
      " [-3.53973889 -2.89384326]\n",
      " [-2.29801424  2.79388557]\n",
      " [ 2.89375324  2.72741571]]\n",
      "[[ 2.65077367 -2.79019029]\n",
      " [-3.53973889 -2.89384326]\n",
      " [-2.46154315  2.78737555]\n",
      " [ 2.6265299   3.10868015]]\n"
     ]
    }
   ],
   "source": [
    "myCentroids, clustAssing = kMeans(datMat, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 2.65077367, -2.79019029],\n",
       "        [-3.53973889, -2.89384326],\n",
       "        [-2.46154315,  2.78737555],\n",
       "        [ 2.6265299 ,  3.10868015]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myCentroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[  3.        ,   2.3201915 ],\n",
       "        [  2.        ,   1.39004893],\n",
       "        [  0.        ,   7.46974076],\n",
       "        [  1.        ,   3.60477283],\n",
       "        [  3.        ,   2.7696782 ],\n",
       "        [  2.        ,   2.80101213],\n",
       "        [  0.        ,   5.10287596],\n",
       "        [  1.        ,   1.37029303],\n",
       "        [  3.        ,   2.29348924],\n",
       "        [  2.        ,   0.64596748],\n",
       "        [  0.        ,   1.72819697],\n",
       "        [  1.        ,   0.60909593],\n",
       "        [  3.        ,   2.51695402],\n",
       "        [  2.        ,   0.13871642],\n",
       "        [  0.        ,   9.12853034],\n",
       "        [  0.        ,  10.63785781],\n",
       "        [  3.        ,   2.39726914],\n",
       "        [  2.        ,   3.1024236 ],\n",
       "        [  0.        ,   0.40704464],\n",
       "        [  1.        ,   0.49023594],\n",
       "        [  3.        ,   0.13870613],\n",
       "        [  2.        ,   0.510241  ],\n",
       "        [  0.        ,   0.9939764 ],\n",
       "        [  1.        ,   0.03195031],\n",
       "        [  3.        ,   1.31601105],\n",
       "        [  2.        ,   0.90820377],\n",
       "        [  0.        ,   0.54477501],\n",
       "        [  1.        ,   0.31668166],\n",
       "        [  3.        ,   0.21378662],\n",
       "        [  2.        ,   4.05632356],\n",
       "        [  0.        ,   4.44962474],\n",
       "        [  1.        ,   0.41852436],\n",
       "        [  3.        ,   0.47614274],\n",
       "        [  2.        ,   1.5441411 ],\n",
       "        [  0.        ,   6.83764117],\n",
       "        [  1.        ,   1.28690535],\n",
       "        [  3.        ,   4.87745774],\n",
       "        [  2.        ,   3.12703929],\n",
       "        [  0.        ,   0.05182929],\n",
       "        [  1.        ,   0.21846598],\n",
       "        [  3.        ,   0.8849557 ],\n",
       "        [  2.        ,   0.0798871 ],\n",
       "        [  0.        ,   0.66874131],\n",
       "        [  1.        ,   3.80369324],\n",
       "        [  3.        ,   0.09325235],\n",
       "        [  2.        ,   0.91370546],\n",
       "        [  0.        ,   1.24487442],\n",
       "        [  1.        ,   0.26256416],\n",
       "        [  3.        ,   0.94698784],\n",
       "        [  2.        ,   2.63836399],\n",
       "        [  0.        ,   0.31170066],\n",
       "        [  1.        ,   1.70528559],\n",
       "        [  3.        ,   5.46768776],\n",
       "        [  2.        ,   5.73153563],\n",
       "        [  0.        ,   0.22210601],\n",
       "        [  1.        ,   0.22758842],\n",
       "        [  3.        ,   1.32864695],\n",
       "        [  2.        ,   0.02380325],\n",
       "        [  0.        ,   0.76751052],\n",
       "        [  1.        ,   0.59634253],\n",
       "        [  3.        ,   0.45550286],\n",
       "        [  2.        ,   0.01962128],\n",
       "        [  0.        ,   2.04544706],\n",
       "        [  1.        ,   1.72614177],\n",
       "        [  3.        ,   1.2636401 ],\n",
       "        [  2.        ,   1.33108375],\n",
       "        [  0.        ,   0.19026129],\n",
       "        [  1.        ,   0.83327924],\n",
       "        [  3.        ,   0.09525163],\n",
       "        [  2.        ,   0.62512976],\n",
       "        [  0.        ,   0.83358364],\n",
       "        [  1.        ,   1.62463639],\n",
       "        [  3.        ,   6.39227291],\n",
       "        [  2.        ,   0.20120037],\n",
       "        [  0.        ,   4.12455116],\n",
       "        [  1.        ,   1.11099937],\n",
       "        [  3.        ,   0.07060147],\n",
       "        [  2.        ,   0.2599013 ],\n",
       "        [  0.        ,   4.39510824],\n",
       "        [  1.        ,   1.86578044]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustAssing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "def showPlt(datMat, alg=kMeans, numClust=4):\n",
    "    myCentroids, clustAssing = alg(datMat, numClust)\n",
    "    fig = plt.figure()\n",
    "    rect=[0.1,0.1,0.8,0.8]\n",
    "    scatterMarkers=['s', 'o', '^', '8', 'p', \\\n",
    "                    'd', 'v', 'h', '>', '<']\n",
    "    axprops = dict(xticks=[], yticks=[])\n",
    "    ax0=fig.add_axes(rect, label='ax0', **axprops)\n",
    "    ax1=fig.add_axes(rect, label='ax1', frameon=False)\n",
    "    for i in range(numClust):\n",
    "        ptsInCurrCluster = datMat[nonzero(clustAssing[:,0].A==i)[0],:]\n",
    "        markerStyle = scatterMarkers[i % len(scatterMarkers)]\n",
    "        ax1.scatter(ptsInCurrCluster[:,0].flatten().A[0], ptsInCurrCluster[:,1].flatten().A[0], marker=markerStyle, s=90)\n",
    "    ax1.scatter(myCentroids[:,0].flatten().A[0], myCentroids[:,1].flatten().A[0], marker='+', s=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.7003961  -3.06412409]\n",
      " [-4.77330446 -1.62940307]\n",
      " [-2.4392688   4.24364984]\n",
      " [ 3.16236716 -0.54814428]]\n",
      "[[ 2.28769    -3.23832819]\n",
      " [-3.4967025  -2.70989515]\n",
      " [-1.58985183  3.13036888]\n",
      " [ 3.15991985  1.76131985]]\n",
      "[[ 2.56468005 -2.8885874 ]\n",
      " [-3.53973889 -2.89384326]\n",
      " [-2.46154315  2.78737555]\n",
      " [ 2.70967829  2.9214931 ]]\n",
      "[[ 2.65077367 -2.79019029]\n",
      " [-3.53973889 -2.89384326]\n",
      " [-2.46154315  2.78737555]\n",
      " [ 2.6265299   3.10868015]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11195e358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showPlt(datMat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def biKmeans(dataSet, k, distMeas=distEclud):\n",
    "    m = shape(dataSet)[0]\n",
    "    clusterAssment = mat(zeros((m,2)))\n",
    "    centroid0 = mean(dataSet, axis=0).tolist()[0]\n",
    "    centList =[centroid0] #create a list with one centroid\n",
    "    for j in range(m):#calc initial Error\n",
    "        clusterAssment[j,1] = distMeas(mat(centroid0), dataSet[j,:])**2\n",
    "    while (len(centList) < k):\n",
    "        lowestSSE = inf\n",
    "        for i in range(len(centList)):\n",
    "            ptsInCurrCluster = dataSet[nonzero(clusterAssment[:,0].A==i)[0],:]#get the data points currently in cluster i\n",
    "            centroidMat, splitClustAss = kMeans(ptsInCurrCluster, 2, distMeas)\n",
    "            sseSplit = sum(splitClustAss[:,1])#compare the SSE to the currrent minimum\n",
    "            sseNotSplit = sum(clusterAssment[nonzero(clusterAssment[:,0].A!=i)[0],1])\n",
    "            print(\"sseSplit, and notSplit: \",sseSplit,sseNotSplit)\n",
    "            if (sseSplit + sseNotSplit) < lowestSSE:\n",
    "                bestCentToSplit = i\n",
    "                bestNewCents = centroidMat\n",
    "                bestClustAss = splitClustAss.copy()\n",
    "                lowestSSE = sseSplit + sseNotSplit\n",
    "        bestClustAss[nonzero(bestClustAss[:,0].A == 1)[0],0] = len(centList) #change 1 to 3,4, or whatever\n",
    "        bestClustAss[nonzero(bestClustAss[:,0].A == 0)[0],0] = bestCentToSplit\n",
    "        print('the bestCentToSplit is: ',bestCentToSplit)\n",
    "        print('the len of bestClustAss is: ', len(bestClustAss))\n",
    "        centList[bestCentToSplit] = bestNewCents[0,:].tolist()[0]#replace a centroid with two best centroids \n",
    "        centList.append(bestNewCents[1,:].tolist()[0])\n",
    "        clusterAssment[nonzero(clusterAssment[:,0].A == bestCentToSplit)[0],:]= bestClustAss#reassign new clusters, and SSE\n",
    "    return mat(centList), clusterAssment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "datMat3=mat(loadDataSet('testSet2.txt'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-4.59366365  1.12766914]\n",
      " [ 0.12133941 -3.71736138]]\n",
      "[[-1.6939583   3.41367307]\n",
      " [ 1.09919724 -0.5651323 ]]\n",
      "[[-1.47714714  3.46545543]\n",
      " [ 0.99677359 -0.73477953]]\n",
      "[[-1.3277349   3.46607079]\n",
      " [ 0.93680474 -0.87084665]]\n",
      "[[-0.84735206  3.46862312]\n",
      " [ 0.63042504 -1.33843332]]\n",
      "[[-0.26853357  3.36606168]\n",
      " [ 0.02053813 -2.21845543]]\n",
      "[[-0.06953469  3.29844341]\n",
      " [-0.32150057 -2.62473743]]\n",
      "[[-0.00675605  3.22710297]\n",
      " [-0.45965615 -2.7782156 ]]\n",
      "sseSplit, and notSplit:  453.033489581 0.0\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  60\n",
      "[[ 0.77244336  3.40609614]\n",
      " [ 4.20217842  3.60121347]]\n",
      "[[-1.71958878  3.23919956]\n",
      " [ 3.55066577  3.20197931]]\n",
      "[[-2.94737575  3.3263781 ]\n",
      " [ 2.93386365  3.12782785]]\n",
      "sseSplit, and notSplit:  77.5922493178 29.1572494441\n",
      "[[ 0.03266217 -2.5433769 ]\n",
      " [-0.29557721 -2.05736765]]\n",
      "[[ 0.07973025 -3.24942808]\n",
      " [-1.26873575 -2.07139688]]\n",
      "[[ 0.19848727 -3.24320436]\n",
      " [-1.26405367 -2.209896  ]]\n",
      "[[ 0.2642961 -3.3057243]\n",
      " [-1.1836084 -2.2507069]]\n",
      "[[ 0.35496167 -3.36033556]\n",
      " [-1.12616164 -2.30193564]]\n",
      "sseSplit, and notSplit:  12.7532631369 423.876240137\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  40\n"
     ]
    }
   ],
   "source": [
    "centList,myNewAssments=biKmeans(datMat3, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[-2.94737575,  3.3263781 ],\n",
       "        [-0.45965615, -2.7782156 ],\n",
       "        [ 2.93386365,  3.12782785]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.38114083 -2.94099986]\n",
      " [ 3.3747977   0.74405553]]\n",
      "[[-1.10426682 -2.4955052 ]\n",
      " [ 0.8970404   2.6041076 ]]\n",
      "[[-0.60201505 -2.84149005]\n",
      " [ 0.37047634  2.80883434]]\n",
      "[[-0.2897198  -2.83942545]\n",
      " [ 0.08249337  2.94802785]]\n",
      "sseSplit, and notSplit:  792.916856537 0.0\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  80\n",
      "[[ 2.05624957 -2.41657702]\n",
      " [-1.77710416 -3.3672664 ]]\n",
      "[[ 2.80293085 -2.7315146 ]\n",
      " [-3.38237045 -2.9473363 ]]\n",
      "sseSplit, and notSplit:  83.5874695564 326.284075201\n",
      "[[ 4.09096075  3.47184925]\n",
      " [-3.17260888  4.24875231]]\n",
      "[[ 2.6265299   3.10868015]\n",
      " [-2.46154315  2.78737555]]\n",
      "sseSplit, and notSplit:  66.36683512 466.632781336\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  40\n",
      "[[ 4.80866852 -0.65326342]\n",
      " [ 2.65718825 -1.6946873 ]]\n",
      "[[ 4.589752   -1.575316  ]\n",
      " [ 2.35622556 -3.02056425]]\n",
      "sseSplit, and notSplit:  28.3386015678 358.885318066\n",
      "[[-2.16126636  3.98922886]\n",
      " [ 2.48681307  3.28990432]]\n",
      "[[-2.46154315  2.78737555]\n",
      " [ 2.6265299   3.10868015]]\n",
      "sseSplit, and notSplit:  66.36683512 83.5874695564\n",
      "[[-4.14302197 -4.183731  ]\n",
      " [-2.66746738 -1.99615953]]\n",
      "[[-3.96952412 -3.4319155 ]\n",
      " [-2.99093467 -2.6242835 ]]\n",
      "[[-4.279292   -3.17607087]\n",
      " [-2.78442275 -2.79484658]]\n",
      "[[-4.35315562 -3.03353825]\n",
      " [-2.73518033 -2.88986833]]\n",
      "[[-4.277741   -2.94771   ]\n",
      " [-2.64979455 -2.94703055]]\n",
      "[[-4.1986774 -2.8253822]\n",
      " [-2.5660635 -3.0692904]]\n",
      "[[-4.12637645 -2.82110091]\n",
      " [-2.47302978 -3.101624  ]]\n",
      "[[-4.04883533 -2.77633633]\n",
      " [-2.38267313 -3.20383625]]\n",
      "sseSplit, and notSplit:  18.3987499855 377.270301893\n",
      "the bestCentToSplit is:  1\n",
      "the len of bestClustAss is:  40\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112fb2d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showPlt(datMat, alg=biKmeans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.6769347  -1.95127745]\n",
      " [ 3.29137484 -3.2280058 ]\n",
      " [-0.58319565 -4.01778134]\n",
      " [ 1.25381349  4.83174706]]\n",
      "[[-0.64622436 -1.28938073]\n",
      " [        nan         nan]\n",
      " [-0.56924375 -3.16951017]\n",
      " [ 0.12097373  3.39830046]]\n",
      "[[-1.07964383 -0.6676455 ]\n",
      " [        nan         nan]\n",
      " [-0.26162575 -3.11178156]\n",
      " [ 0.03159237  3.35037329]]\n",
      "[[-1.39334014 -0.34697214]\n",
      " [        nan         nan]\n",
      " [-0.26162575 -3.11178156]\n",
      " [ 0.12097373  3.39830046]]\n",
      "[[-2.29139617  0.3190665 ]\n",
      " [        nan         nan]\n",
      " [-0.35159365 -3.05723659]\n",
      " [ 0.27735416  3.33995757]]\n",
      "[[-3.08714142  2.17369333]\n",
      " [        nan         nan]\n",
      " [-0.36852161 -2.96793856]\n",
      " [ 1.14052403  3.36194603]]\n",
      "[[-3.06779095  3.33769884]\n",
      " [        nan         nan]\n",
      " [-0.45965615 -2.7782156 ]\n",
      " [ 2.76275171  3.12704005]]\n",
      "[[-2.94737575  3.3263781 ]\n",
      " [        nan         nan]\n",
      " [-0.45965615 -2.7782156 ]\n",
      " [ 2.93386365  3.12782785]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/numpy/matrixlib/defmatrix.py:549: RuntimeWarning: Mean of empty slice.\n",
      "  return N.ndarray.mean(self, axis, dtype, out, keepdims=True)._collapse(axis)\n",
      "/usr/local/lib/python3.6/site-packages/numpy/core/_methods.py:73: RuntimeWarning: invalid value encountered in true_divide\n",
      "  ret, rcount, out=ret, casting='unsafe', subok=False)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113107390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showPlt(datMat3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-3.73015388  4.33641686]\n",
      " [ 0.35020953  0.14475902]]\n",
      "[[-3.06779095  3.33769884]\n",
      " [ 1.19084544  0.24642754]]\n",
      "[[-2.94737575  3.3263781 ]\n",
      " [ 1.23710375  0.17480612]]\n",
      "sseSplit, and notSplit:  570.722757425 0.0\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  60\n",
      "[[-1.03503509  4.6965156 ]\n",
      " [-1.29373783  3.87575266]]\n",
      "[[        nan         nan]\n",
      " [-2.94737575  3.3263781 ]]\n",
      "sseSplit, and notSplit:  38.0629506357 532.659806789\n",
      "[[ 2.03524785  4.10899772]\n",
      " [-0.10492573  0.86532019]]\n",
      "[[ 2.95977168  3.26903847]\n",
      " [-0.32150057 -2.62473743]]\n",
      "[[ 2.93386365  3.12782785]\n",
      " [-0.45965615 -2.7782156 ]]\n",
      "sseSplit, and notSplit:  68.6865481262 38.0629506357\n",
      "the bestCentToSplit is:  1\n",
      "the len of bestClustAss is:  40\n",
      "[[-4.18665331  4.80290016]\n",
      " [-4.21454599  4.79208298]]\n",
      "[[-2.59844753  3.47589133]\n",
      " [-3.9941604   2.8778384 ]]\n",
      "[[-2.43131608  3.45250362]\n",
      " [-3.90577229  3.092145  ]]\n",
      "[[-2.1459026  3.421808 ]\n",
      " [-3.7488489  3.2309482]]\n",
      "[[-1.9062885   3.42271988]\n",
      " [-3.64143392  3.26215025]]\n",
      "[[-1.76576557  3.39794014]\n",
      " [-3.58362738  3.28784469]]\n",
      "sseSplit, and notSplit:  22.9717718963 68.6865481262\n",
      "[[ 1.24653343  1.45934856]\n",
      " [ 1.3696721   4.23605202]]\n",
      "[[ 3.1604785   1.93671333]\n",
      " [ 2.836743    3.6383055 ]]\n",
      "sseSplit, and notSplit:  26.9283722645 67.2202000798\n",
      "[[-1.56899016 -3.19395486]\n",
      " [-0.85562372 -0.98205245]]\n",
      "[[-0.39994281 -3.1000235 ]\n",
      " [-0.6985095  -1.490984  ]]\n",
      "[[-0.15366667 -3.15354   ]\n",
      " [-1.3776246  -1.6522424 ]]\n",
      "[[-0.05200457 -3.16610557]\n",
      " [-1.41084317 -1.873139  ]]\n",
      "[[ -7.11923077e-04  -3.21792031e+00]\n",
      " [ -1.31198114e+00  -1.96162114e+00]]\n",
      "[[ 0.07973025 -3.24942808]\n",
      " [-1.26873575 -2.07139688]]\n",
      "[[ 0.19848727 -3.24320436]\n",
      " [-1.26405367 -2.209896  ]]\n",
      "[[ 0.2642961 -3.3057243]\n",
      " [-1.1836084 -2.2507069]]\n",
      "[[ 0.35496167 -3.36033556]\n",
      " [-1.12616164 -2.30193564]]\n",
      "sseSplit, and notSplit:  12.7532631369 77.5922493178\n",
      "the bestCentToSplit is:  2\n",
      "the len of bestClustAss is:  20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/numpy/matrixlib/defmatrix.py:549: RuntimeWarning: Mean of empty slice.\n",
      "  return N.ndarray.mean(self, axis, dtype, out, keepdims=True)._collapse(axis)\n",
      "/usr/local/lib/python3.6/site-packages/numpy/core/_methods.py:73: RuntimeWarning: invalid value encountered in true_divide\n",
      "  ret, rcount, out=ret, casting='unsafe', subok=False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113212320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showPlt(datMat3, alg=biKmeans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib\n",
    "import json\n",
    "def geoGrab(stAddress, city):\n",
    "    apiStem = 'http://where.yahooapis.com/geocode?'  #create a dict and constants for the goecoder\n",
    "    params = {}\n",
    "    params['flags'] = 'J'#JSON return type\n",
    "    params['appid'] = 'aaa0VN6k'\n",
    "    params['location'] = '%s %s' % (stAddress, city)\n",
    "    url_params = urllib.parse.urlencode(params)\n",
    "    yahooApi = apiStem + url_params      #print url_params\n",
    "    print(yahooApi)\n",
    "    c=urllib.request.urlopen(yahooApi)\n",
    "    return json.loads(c.read())\n",
    "\n",
    "from time import sleep\n",
    "def massPlaceFind(fileName):\n",
    "    fw = open('places.txt', 'w')\n",
    "    for line in open(fileName).readlines():\n",
    "        line = line.strip()\n",
    "        lineArr = line.split('\\t')\n",
    "        retDict = geoGrab(lineArr[1], lineArr[2])\n",
    "        if retDict['ResultSet']['Error'] == 0:\n",
    "            lat = float(retDict['ResultSet']['Results'][0]['latitude'])\n",
    "            lng = float(retDict['ResultSet']['Results'][0]['longitude'])\n",
    "            print(\"%s\\t%f\\t%f\" % (lineArr[0], lat, lng))\n",
    "            fw.write('%s\\t%f\\t%f\\n' % (line, lat, lng))\n",
    "        else: print(\"error fetching\")\n",
    "        sleep(1)\n",
    "    fw.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# geoResults = geoGrab('1 VA Center', 'Augusta ME')  #stop working as yahoo API not available any more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distSLC(vecA, vecB):#Spherical Law of Cosines\n",
    "    a = sin(vecA[0,1]*pi/180) * sin(vecB[0,1]*pi/180)\n",
    "    b = cos(vecA[0,1]*pi/180) * cos(vecB[0,1]*pi/180) * \\\n",
    "                      cos(pi * (vecB[0,0]-vecA[0,0]) /180)\n",
    "    return arccos(a + b)*6371.0 #pi is imported with numpy\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "def clusterClubs(numClust=5):\n",
    "    datList = []\n",
    "    for line in open('places.txt').readlines():\n",
    "        lineArr = line.split('\\t')\n",
    "        datList.append([float(lineArr[4]), float(lineArr[3])])\n",
    "    datMat = mat(datList)\n",
    "    myCentroids, clustAssing = biKmeans(datMat, numClust, distMeas=distSLC)\n",
    "    fig = plt.figure()\n",
    "    rect=[0.1,0.1,0.8,0.8]\n",
    "    scatterMarkers=['s', 'o', '^', '8', 'p', \\\n",
    "                    'd', 'v', 'h', '>', '<']\n",
    "    axprops = dict(xticks=[], yticks=[])\n",
    "    ax0=fig.add_axes(rect, label='ax0', **axprops)\n",
    "    imgP = plt.imread('Portland.png')\n",
    "    ax0.imshow(imgP)\n",
    "    ax1=fig.add_axes(rect, label='ax1', frameon=False)\n",
    "    for i in range(numClust):\n",
    "        ptsInCurrCluster = datMat[nonzero(clustAssing[:,0].A==i)[0],:]\n",
    "        markerStyle = scatterMarkers[i % len(scatterMarkers)]\n",
    "        ax1.scatter(ptsInCurrCluster[:,0].flatten().A[0], ptsInCurrCluster[:,1].flatten().A[0], marker=markerStyle, s=90)\n",
    "    ax1.scatter(myCentroids[:,0].flatten().A[0], myCentroids[:,1].flatten().A[0], marker='+', s=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-122.61115562   45.44397846]\n",
      " [-122.55560075   45.39916994]]\n",
      "[[-122.63886224   45.51533564]\n",
      " [-122.47358333   45.44567   ]]\n",
      "[[-122.65758128   45.51763982]\n",
      " [-122.50862708   45.48697433]]\n",
      "[[-122.67148356   45.51848782]\n",
      " [-122.52692      45.49604058]]\n",
      "[[-122.68205147   45.51492282]\n",
      " [-122.53722258   45.50740146]]\n",
      "[[-122.69531692   45.50981882]\n",
      " [-122.54894327   45.51554093]]\n",
      "[[-122.69551477   45.50729503]\n",
      " [-122.54868607   45.51882187]]\n",
      "sseSplit, and notSplit:  3043.26331581 0.0\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  69\n",
      "[[-122.70779019   45.47535942]\n",
      " [-122.75874658   45.55652191]]\n",
      "[[-122.68703723   45.48856387]\n",
      " [-122.72836525   45.57987825]]\n",
      "[[-122.69000022   45.48917759]\n",
      " [-122.72072414   45.59011757]]\n",
      "sseSplit, and notSplit:  1435.43784877 851.438888564\n",
      "[[-122.43626548   45.5416294 ]\n",
      " [-122.38700197   45.47913571]]\n",
      "[[-122.55463028   45.52187369]\n",
      " [-122.376304     45.430319  ]]\n",
      "[[-122.55924018   45.52238271]\n",
      " [-122.4009285    45.46897   ]]\n",
      "sseSplit, and notSplit:  501.328788157 2191.82442724\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  39\n",
      "[[-122.7326706    45.50925728]\n",
      " [-122.62108425   45.52271223]]\n",
      "[[-122.761804     45.46639582]\n",
      " [-122.65238871   45.5011109 ]]\n",
      "sseSplit, and notSplit:  602.700428436 1051.39643532\n",
      "[[-122.55631578   45.45440825]\n",
      " [-122.59171124   45.48063201]]\n",
      "[[-122.49449767   45.49126589]\n",
      " [-122.57190967   45.53063157]]\n",
      "[[-122.49860291   45.49496564]\n",
      " [-122.57768158   45.53263337]]\n",
      "sseSplit, and notSplit:  464.720598381 1435.43784877\n",
      "[[-122.71255168   45.55631344]\n",
      " [-122.76235138   45.58710031]]\n",
      "[[-122.68609125   45.57347025]\n",
      " [-122.76690133   45.612314  ]]\n",
      "sseSplit, and notSplit:  100.232895223 2086.91919058\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  32\n",
      "[[-122.76254617   45.4113734 ]\n",
      " [-122.73807934   45.43400338]]\n",
      "[[-122.759843     45.404235  ]\n",
      " [-122.76223978   45.48020933]]\n",
      "sseSplit, and notSplit:  117.645985879 1419.59696299\n",
      "[[-122.44921074   45.54737199]\n",
      " [-122.56004983   45.55645484]]\n",
      "[[-122.4568086    45.4961344 ]\n",
      " [-122.56706156   45.52335936]]\n",
      "sseSplit, and notSplit:  505.61960813 802.657975192\n",
      "[[-122.79078375   45.55669159]\n",
      " [-122.675234     45.62878001]]\n",
      "[[-122.781873    45.5949235]\n",
      " [-122.6962646   45.5881952]]\n",
      "[[-122.842918     45.646831  ]\n",
      " [-122.7003585    45.58066533]]\n",
      "sseSplit, and notSplit:  48.1802401385 1454.139317\n",
      "[[-122.66663512   45.41833731]\n",
      " [-122.66152267   45.44865888]]\n",
      "[[-122.626526     45.413071  ]\n",
      " [-122.65511111   45.51037826]]\n",
      "sseSplit, and notSplit:  147.35464359 1285.89633609\n",
      "the bestCentToSplit is:  1\n",
      "the len of bestClustAss is:  30\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11334ea58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusterClubs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
